Disorders of sexual development (DSDs) often result from defects in the process of primary sex determination and/or establishment of steroidogenesis in the bipotential gonad. The gonad primordium is initially balanced between testis and ovarian fates by antagonistic signals. In XY embryos, this balance is disrupted by expression of Sry, which activates genes that promote the testis and oppose the ovarian pathways. While roles for a few genes have been defined by mutation, >50% of human DSD cases are unexplained by known genes, and likely involve unknown players in the sex determination network. In a pilot study, we measured expression levels in an intercross F2 (B6 x 12981) panel and conducted a first order conditional independence analysis. This work defined a male and female sub-network, and suggested critical nodes In the connectivity map among the 54 genes in our study. To expand this analysis, we plan to conduct RNAseq on sorted XX and XY supporting and steroidogenic cell precursors at eight timepoints between E11.5-E13.5, the period when primary sex determination occurs. To define the temporal relationship between genes, we will train a Hidden Markov model on this dataset to identify genes that are activated or repressed in sequential cascades during initiation of the male or female pathways. We will integrate results with DNasel hypersensitivity maps of El 3.5 XX and XY supporting and steroidogenic cell precursors to predict interactions between transcription factors and downstream regulatory regions. Key predictions will be tested using ChIP and gain and loss of function approaches. We will then use this information to target exome sequencing of human DSD patients in non-coding regions of genes that we identify as key regulators of sexual development. Subsequently, we will validate these findings using physiologically relevant transient reporter assays. This novel approach will establish a new paradigm to predict the proximate effects of allelic variants on the sex determination network, and use this data to inform the iterative ranking of allelic variants identified in human DSD patients based on their likelihood of causation. This work will lead immediately to improvements in diagnosis, clinical assessment, and treatment decisions for DSD patients.